Tom Lynn's Blog: Why statistical information can only take you so far

Joe Sakic scored 54 goals in 2000-01, but had only one more even strength marker than Antti Laaksonen. (Photo by Doug Pensinger/Getty Images)

Tom Lynn
2009-08-28 09:15:00

Since the popular book “Moneyball” changed the way we look at baseball, its influence has gradually spread to the other major professional sports.

The current vogue idea in sports management circles is that exhaustive data and the right equation can produce the optimal management of professional athletes and their team. More directly, many want to believe that with enough statistics and a computer, a math geek as GM could make Sam Pollock look as bumbling as Inspector Clouseau. Fortunately for those flesh-and-blood hockey fans out there, we are not as close to hockey being taken over by computers from The Matrix as you might fear.

While assistant GM with the Minnesota Wild, for many years I would receive proposals from a wide variety of professionals, professors and students, each purporting to have developed a mathematical measurement of player performance that would ensure success on the ice. The common theme was the seller’s analytical product would take the unscientific “guesswork” out of management’s decision-making and create the perfect team.

We never pursued any of those products because the NHL already had developed and distributed an incredible amount of statistical information about the game; the approaches tended to be hindsight oriented, meaning they were designed to explain events that happened in the past, rather than what is going to happen in the future. Put another way, it is much easier to develop a scientific explanation for how a tornado developed than it would be to accurately predict the next one.

In the same way, many analysts can claim to explain the relative success and failures of the top players in the 2003 draft compared to those from 2005, but cannot establish with reasonable certainty the top 30 players for 2011. The same problems tend to dog predictive models for established NHL players – easy to explain the past, difficult to predict the future, as statistics by themselves can be misleading.

The key to understanding the usefulness of statistics is context. I heard an argument in a salary arbitration case years ago that Antti Laaksonen, then one of the better defensive wingers in the game, had only one less even-strength goal than Joe Sakic in 2000-2001, so the only thing standing between Laaksonen and superstardom was Sakic’s five minutes in power play time per game.

As much as that argument made sense on paper, it was not credible for anyone who had watched those teams play that season.

In another example of misleading stats, the Wild acquired the late Sergei Zholtok from Edmonton in June of 2001 for a ninth round draft pick, even though Sergei had registered 26 goals for the Montreal Canadiens just two seasons prior. In those low-scoring years, a player with that many goals would normally have fetched a much higher price. On closer examination, however, one found that the Canadiens’ top two centers, Saku Koivu and Trevor Linden, had been injured much of the 1999-2000 season and Zholtok had received all of the offensive center ice time during that stretch, giving him the opportunity for a career year. His expected contributions in a normal situation would have been less.

In both of these situations, the context of the player’s stats played more of a part in determining the player’s value than the stats themselves. A mathematician GM with no hockey background would not have been able to make those distinctions.

There is a big difference between knowledge and information; one I will tackle in a blog next week.

Tom Lynn served for nine seasons as assistant GM of the Minnesota Wild and for six seasons as GM of the Houston Aeros. Prior to that he was an attorney in New York representing the NHL and other sports entities in a wide variety of legal matters and has taught Sports Law at St. Thomas Law School in Minnesota. Read more from Lynn HERE.